Cluster sampling refers to a method where the population is divided into groups called clusters. A simple random sample of these clusters is selected, and then all or a subset of elements within the selected clusters are included in the final sample. It is cheaper than simple random sampling but has a higher chance of sampling error. The key aspects are that the population is divided into clusters, a random sample of clusters is taken, and then data is collected from elements within those clusters.

1. CLUSTER
SAMPLING
PRESENTED BY
SHAHILA SHAHUL

2.  Cluster sampling refers to a type of sampling
method . With cluster sampling, the
researcher divides the population into
separate groups, called clusters. Then, a
simple random sample of clusters is selected
from the population. The researcher conducts
his analysis on data from the sampled
clusters.

3. Cluster sampling refers to a sampling method that
has the following properties.
 The population is divided into N groups,
called clusters.
 The researcher randomly selects n clusters to
include in the sample.
 The number of observations within each
cluster Mi is known, and M = M1 + M2 + M3 + ... +
MN-1 + MN.
 Each element of the population can be assigned
to one, and only one, cluster.

4. TYPE OF SAMPLING
 One-stage sampling. All of the elements within selected
clusters are included in the sample.
 Two-stage sampling. A subset of elements within
selected clusters are randomly selected for inclusion in
the sample.

5.  Applications are area sampling
or geographcal cluster sampling
 Advantages are cheaper ,large Feasibility
study,Economy, Reduced variability
 Disdvantages are high chance of sampling
error.